a novel algorithm for profit distribution of sustainable

Research ArticleA Novel Algorithm for Profit Distribution of SustainableDevelopment Using E-Commerce Supply Chain

Li Wei 12

1Business School Yangzhou Polytechnic Institute Yangzhou 225000 China2e Logistics Institute-Asia Pacific National University of Singapore Singapore

Correspondence should be addressed to Li Wei liwypieducn

Received 31 August 2021 Revised 13 September 2021 Accepted 18 September 2021 Published 11 October 2021

Academic Editor Fazlullah Khan

Copyright copy 2021 Li Wei )is is an open access article distributed under the Creative Commons Attribution License whichpermits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

E-commerce supply chain actually deals with the acquisition of the raw materials their timely processing and on-time delivery tothe right place It deals with a number of processes such as supply and demand managing order entry and inventory trackingNow profit distribution models are actually stochastic models that are used to optimize the gains and profits in a particularbusiness )ey actually generate modern time solutions to the existing problems in a sustainable environment However in orderto solve the problems existing in the traditional profit distribution algorithm of the e-commerce supply chain such as lowdistribution accuracy and large time cost a profit distribution algorithm of the e-commerce supply chain under the concept ofsustainable development was designed It was supported by the concept of sustainable development and the coefficient of incomedistribution is calculated according to the equilibrium bidding strategy of supply chain alliance and its members net present valueof income distribution total investment minimum expected rate of return on investment and other parameters First calculatethe Shapley value of the profit distribution of the power supply chain and obtain the correction coefficient through the correctionmatrix dimensionless processing and analytic hierarchy process Use the correction coefficient to correct the Shapley value andthe income distribution coefficient to realize the design of the profit distribution algorithm for the power supply chain )eexperimental results show that the algorithm has low relative error rate high precision and short time cost of profit distributioncoefficient calculation

1 Introduction

With the rapid development of the Internet electronic in-formation and mobile terminal technology e-commerce asan emerging industry has developed rapidly bringing manydevelopment opportunities and creating a wealth myth [1]As a result a large number of enterprises flood into thee-commerce market and participate in the competition ase-commerce enterprises With the deepening of economicglobalization the market competition among e-commerceenterprises has increasingly reached the white-hot stage andits competition mode has been transformed from the tra-ditional individual competition to the competition in thewhole supply chain of enterprises [2] Each participant in thee-commerce supply chain is an independent interestclaimant driven by the maximization of interests Each node

in the e-commerce supply chain gives priority to its ownbenefits transfers operational risks and grabs excess returns[3] )e intersection of interests of various stakeholdersoften exists with the game in the pursuit of self-interestmaximization one party not only can damage the interestsof others but also can destroy the stability of the electricitysupply chain structure by making the supply chain coop-eration face uncertainty hindering the supply chain tomaximize the overall interests and highlighting the profitdistribution of supply chain internal contradictions grad-ually )is contradiction seriously hinders the sustainabledevelopment of supply chain cooperation so it is of greatsignificance to study a profit distribution algorithm ofe-commerce supply chain

To solve the above problems a lot of research resultshave emerged in the present stage such as reference [4] to

HindawiMobile Information SystemsVolume 2021 Article ID 1166788 9 pageshttpsdoiorg10115520211166788

design a kind of electricity supply chain based on cloudgravity method benefit allocation algorithm )e algorithmmainly studies the influence members of the electricitysupply chain financing income distribution the main factorsand the level of risk the degree of information sharing theeffort level and the audit supervision costs into the cloudgravity method On this basis the revenue distributionmodel is built to complete the design of the profit distri-bution algorithm of the e-commerce supply chain Howeverthe time cost of the profit distribution algorithm of thee-commerce supply chain is large and the overall efficiency islow Reference [5] designs a benefit distribution algorithm ofe-commerce supply chain based on block chain technologySix factors including resource contribution status effectrisk taking additional subsidy implementation degree andinnovation effort are proposed to influence benefit distri-bution and then a modified Shapley value model is estab-lished At the same time blockchain technology is applied tosolve problems such as mistrust among supply chainmembers and difficulties inmonitoring transaction data)einformation transparency and immutability provided byblockchain make the revised profit distribution strategymore fair credible and enforceable However the calcu-lation of the coefficient of income distribution of the al-gorithm has relatively low error rate so the practicalapplication effect is not good

At this stage the relevant leaders have stressed in manyoccasions on the concept of sustainable development whichrequires the electricity in lower costs At the same time itmust pay attention to the scientific methods and cannot relyon the expense of the damage to the ecosystem in order torealize the coordination of enterprise society environmentand sustainable development )is is the concept of sus-tainable development as the basic requirement of the electricbusiness enterprise )erefore this paper designs ane-commerce supply chain benefit distribution algorithmunder the concept of sustainable development and verifiesthe effectiveness of the algorithm through experiments

2 Design of Profit Distribution Algorithm forE-Commerce Supply Chain

)e word ldquobenefitrdquo has many different meanings and eventhe interpretation of the benefit varies widely in differentenvironments Other relevant experts and scholars havedefined this that is interest belongs to the category of socialrelations in essence and it is a kind of need in essence whichcan satisfy the psychological and behavioral demands of allclasses and groups All groups obtain psychological andbehavioral satisfaction to maintain their own survival anddevelopment which can meet the needs of different groupsof objects Profit distribution refers to the fact that in variouscooperative relationships the participants share their dueshare from the total income or profits generated by coop-eration [6] )e benefit distribution of the supply chainmeans that each member of the supply chain takes its dueshare from the total profit or revenue created by the supplychain alliance Of course each member of the supply chainshould reasonably share the total cost of the supply chain [7]

)e profit distribution of supply chain in this paper mainlyrefers to the profit increased by participating in cooperationCooperation is the prerequisite of benefit distribution and areasonable benefit distribution scheme is the guarantee ofstable cooperative relationship For supply chain membersjoining the supply chain alliance can not only enjoy variousshared resources brought by collaboration but also improveefficiency and reduce costs so as to gain more benefits [8]However combined with the current situation of e-com-merce supply chain cooperation its profit distributionmodel is often difficult to put into practice technically)erefore how to make a reasonable benefit distributionscheme of supply chain has become an imminent problem ofsupply chain management

21 Calculation of Income Distribution Coefficient )esupply chain alliance and its members distribute the cost andbenefit and the final amount allocated by both parties mustnot be lower than the basic expectation of each party beforeit can be accepted by both parties From the rational manhypothesis in the bargaining theory maximizing their ownbenefits is the code of conduct for both parties In order togain a beneficial amount of benefits both parties generallywill not easily choose to compromise [9] In the process ofbargaining both parties take turns to bid One party makesthe first bid and the other party can reject or accept it Onlywhen one partyrsquos bid is accepted by the other party thebargaining process ends that is both parties reach thedistribution amount satisfying their own interests [10]Suppose that after this process the equilibrium biddingstrategy of supply chain alliance and its members is obtainedwhich is represented by Pc1 and Pe1prime respectively

)ere will be three situations when the size of Pc1 + Pe1primeis compared with the size of revenue cakeNPV(P) minus I(1 + R) where NPV(P) represents the netpresent value of the income distribution I represents thetotal investment and R represents the minimum expectedreturn on investment

Case 1 Pc1 + Pe1prime is less than cake NPV(P) minus I(1 + R)Both the supply chain alliance and its members have a

certain understanding of the relevant cost data or benefits soany party in its first bid its bidding strategy set will refer to thedata or benefits they have obtained At this time the sum of thebidding strategy values of the supply chain alliance and itsmembers in the case of their first bid Pc1 + Pe1prime is probably lessthan the total cake [11] In this way I(1 + R) + Pc1 can bedefined as the lower limit of the income distribution intervaland the upper limit of the new income distribution interval anda new income distribution interval (I(1 + R) + Pc1NPV(P) minus

Pe1prime ) can be obtained which is Pc1 + Pe1prime smaller than theoriginal income distribution interval

Under the new income distribution interval repeat theabove analysis process that is distribute the ldquoreduced in-come cakerdquo again and a new income distribution intervalwill be obtained In Rubensteinrsquos bargaining model [12] thepatience factor is introduced that is with each incomedistribution the reduction of ldquocakerdquo and the extension of

2 Mobile Information Systems

time the patience degree of both the supply chain allianceand its members will decrease and their discount factor willalso decrease continuously [13] )erefore the interval ofincome distribution will accelerate convergence and even-tually converge to a certain point theoretically Assumingthat the convergence point is reached after n rounds ofbargaining the formula is as follows

NPV(P) minus Pe1prime minus Pe2prime minus middot middot middot minus Penprime I(1 + R)

+ Pc1 + Pc2 + middot middot middot + Pcn

(1)

Although the convergence point can be achieved theo-retically it is very difficult to achieve the convergence pointin reality Generally speaking when the ratio of a new in-come distribution interval to the size of the initial incomedistribution ldquocakerdquo is less than a certain value (set as anacceptable precision level of σ ) [14] the discounted value ofboth parties will reach a very low value and both parties willnot care much about the unallocated cake So the two sideswill agree on any point in the new range In other wordsafter h rounds of income bargaining (h less than n) if thenew income distribution interval meets the acceptable ac-curacy level σ the two sides of the new income distributionwill be considered as required and the income distributionwill reach an agreement )e formula is as follows

NPV(P) minus Pe1prime minus Pe2prime minus middot middot middot minus Pehprime1113858 1113859 minus IR + Pc1 + Pc2 + middot middot middot + Pch

NPV(P) minus I(1 + R)

(2)

When the acceptable accuracy level σ is reached the newincome distribution interval is

I(1 + R) + Pc1 + Pc2 + middot middot middot + PchNPV minus Pe1prime minus Pe2prime minus middot middot middot minus Pehprime( 1113857

(3)

At this point the income distribution value points of thesupply chain alliance and its members can be selected at anypoint within this range It is assumed that the proportion ofthe benefits of the supply chain alliance from the new in-terval to the size of the new interval is α where the size of thenew interval is

C NPV(P) minus Pe1prime minus Pe2prime minus middot middot middot minus Pehprime( 1113857

minus I(1 + R) + Pc1 + Pc2 + middot middot middot + Pch( 1113857( 1113857(4)

At this time the supply chain alliancersquos allocationfrom the original cake NPV(P) minus I(1 + R) is calculated asfollows

Plowastc 1113944

h

i1Pci + αC (5)

Membersrsquo allocations from the original cake NPV(P) minus

I(1 + R) were

Plowaste 1113944

h

i1Pei + αC (6)

At this time the income distribution strategy is (Plowastc Plowaste )

Case 2 Pc1 + Pe1prime is equal to cake NPV(P) minus I(1 + R))e sum of Pc1 + Pe1prime of the bidding strategy values of the

supply chain alliance and its members in the case of the firstbid is equal to the total cake NPV(P) minus I(1 + R) At thispoint the supply chain alliance and its members reach thevalue point agreed by both parties on revenue distributionthat is reach an agreement on revenue distribution [15] Atthis point the profit distribution value point of both partiesis NPV(P) minus Pe1prime or I(1 + R) + Pc1 and the profit distribu-tion strategy is (Pc1 Pe1prime ) But the probability of being exactlyequal is very low and even if it does occur both parties willshare the same amount of revenue cake as in the first case

Case 3 Pc1 + Pe1prime is greater than cake NPV(P) minus I(1 + R)If one or both parties of the supply chain alliance and its

members expect to share an unusually large cake from thebenefit cake of the dual cost control standard it is possiblethat the sum of Pc1 + Pe1prime of the bidding strategy values of thesupply chain alliance and its members in the case of theirfirst bid is greater than the total cake NPV(P) minus I(1 + R)But in fact both the enterprise and the grass-roots em-ployees have a certain understanding of the size of the totalcake and the situation of the other party so the probability ofsuch a situation is very low If this happens both parties willreexamine their own expectations and the expectations ofthe other party as well as the size of the total cake so that thedistribution will develop towards the first situation men-tioned above [16]

To sum up the revenue distribution strategy of supplychain alliance and its members for the total bargainingrevenue cake NPV(P) minus I(1 + R) is (Plowastc Plowaste ) wherein Plowastcrepresents the revenue distribution of supply chain allianceand Plowaste represents the revenue distribution of its members)us it can be seen that the amount allocated in NPV(P) ofthe supply chain alliance is Sc I(1 + R) + Plowastc

)e amount allocated to members from incomeNPV(P) was Se NPV(P) minus [I(1 + R) + Plowastc ] )ereforeunder the concept of sustainable development withsupply chain alliance as the distribution subject andmembers as distribution recipients the income distri-bution coefficient is

b NPV(P) minus I(1 + R) + P

lowastc1113858 1113859

NPV(P) (7)

22 Profit Distribution of E-Commerce Supply ChainShapley value method is a mathematical method proposedby Shapley LS in 1953 to solve n cooperative game problemsWhen n people are engaged in a certain economic activityeach form of cooperation combined by several of them willget certain benefits When the interest activities betweenpeople are not antagonistic the increase of the number ofpeople in the cooperation will not cause the reduction ofbenefits In this way the cooperation of all n people willbring the maximum benefit Shapley value method is ascheme to allocate the maximum benefit which is defined asfollows

Let us set N 1 2 n if for any subset s of N

(representing any union in the set of n members)

Mobile Information Systems 3

corresponds to a real-valued function V(s) and satisfies theconditions

v(ϕ) 0

v s1 cup s2( 1113857geV s1( 1113857 + V s2( 1113857 s1 cap s2 ne ϕ(8)

Suppose [N V] is the cooperative game of n membersV is the characteristic function of the game and V(s) isthe benefit of s cooperation [17] Assuming that Xi

represents the revenue allocated by i members of set N

from the maximum cooperative revenue V(I) and X

(X1 X2 X3 Xn) represents the collective utilizationof the allocation of n membersrsquo cooperative game theconditions for successful cooperation are as follows

W(|s|) (n minus |s|)(|s| minus 1)

n

1113944

n

i1Xi v(I)

Xi ge v(i)

(9)

In the formula s represents the number of membersand Ws represents the weighting factor

When ϕ(v) (ϕ1(v) ϕ2(v) ϕn(v)) is rememberedas the cooperation quantity ϕi(v) represents the profitdistribution (Shapley value) of the ith member of themember )e formula is expressed as follows

ϕi(v) 1113944sisinsi

W |s| v(s) minus vs

i1113874 11138751113874 11138751113876 1113877 (10)

In the formula si represents all subsets containingmembers in the set v(s) represents the income of subset sand v(si) represents the income obtained after removingmembers from the subset

Shapley value method is widely used in the practice ofcooperative benefits It does not evenly distribute ben-efits but distributes them based on the contributiondegree of members in the alliance Shapley value methodis actually the weighted sum of marginal benefits ofmembers [18]

However the classic Shapley value method has manyshortcomings It regards risk taking capital investment andother influencing factors of each node as 1n and ignoresother factors in profit distribution In the case of unequalrisk taking among members the benefit distribution schemeof Shapley value method should be modified appropriatelyaccording to the size of risk taking Based on the existingresearch results this section introduces three influencingfactors of risk taking innovation level and resource inputand revises the benefit distribution scheme of Shapley valuemethod in e-commerce supply chain under the concept ofsustainable development

)e method steps to correct Shapley value are as follows

221 Modified Matrix and Its Dimensionless ProcessingOn the basis of Shapley value the set of cooperative objectsis I 1 2 n the set of correction factors is

J 1 2 m the test value of the jth correction factorcorresponding to the ith cooperative object is aij and thecorrection matrix is represented by A (aij)ntimesm In order toeliminate the commonness caused by attributes and di-mensions among various factors dimensionless processingis carried out on the data of matrix A (aij)ntimesm )eprocessing method is as follows

bij aij minus min aij

max aj minus min aj

(11)

)e normalized benefit distribution correction matrixcan be obtained ie B (bij)ntimesm

B

b11 b12 middot middot middot b1m


⋮ ⋮ ⋱ ⋮

bn1 bn2 middot middot middot bnm

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(12)

222 Using Analytic Hierarchy Process to Determine theWeight of the Correction Factor

Step 1 the first step is to compare scales According tothe judgment principle of pairwise comparison thebinary comparison method is adopted to assign valuesto indicators at the same level and rank them accordingto their importance )e assignment criteria are shownin Table 1 aij is the result of comparing the importanceof index i and index j and aij 1aij existsHierarchical evaluation criteria for the importance ofindicators are shown in Table 1Step 2 the second step is to construct the judgmentmatrix and calculate the weight vector Let the judg-ment matrix be

R

r11 r12 middot middot middot r1m


⋮ ⋮ ⋱ ⋮

rn1 rn2 middot middot middot rnm

⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(13)

)e root method was used to obtain the weight vectorof each index normalize each column of vectors andthe results are as follows

Wijprime

aij

1113936nj1 aij

(14)

)e characteristic vector W and the maximum char-acteristic root λmax were calculated as follows

Wi

1113937

nj1 Wijprimen

1113969

1113936ni1

1113937

nj1 Wijprimen

1113969

λmax 1113944n

i1

(RW)i

nWi

(15)

Step 3 consistency test since the judgment matrix issubjective and one sided errors are inevitable In order


to verify the rationality of weight allocation a con-sistency test is needed for thematrix)e formula of theconsistency test is

CI λmax minus n

n minus 1 (16)

In the formula n is the matrix order and CI is theconsistency test index

CR CIRI

(17)

In the formula RI is the average random consistencyindex and the value standard is shown in Table 2 CR isthe consistency ratio of the judgment matrix WhenCRlt 01 the consistency test requirements are metotherwise the weight needs to be adjusted

223 Calculating the Distribution Results of E-CommerceSupply Chain )e Euclidean distance calculation formula isused to calculate the distance between the evaluation objectand the positive ideal point and the negative ideal pointA+ (a+

1 a+2 a+

n ) is defined as the distance between theevaluation object and the positive ideal point and Aminus

(aminus1 aminus

2 aminusn ) is defined as the distance between the

evaluation object and the negative ideal point

ai+

1113944

m

j1λj bij minus bj

+1113872 1113873

2

11139741113972

aiminus

1113944

m

j1λj bij minus bj

minus1113872 1113873

2

11139741113972

(18)

In the formula λj is the weight of the correction factor jwhich is obtained through analytic hierarchy process

If βi is defined as the closeness of each evaluation objectto the ideal point then

βi a

minusi

a+i + a

minusi

(19)

)e above results are normalized and ci is defined as thenormalized closeness degree )en

ci βi

1113936ni1 βi

(20)

Finally the correction coefficient Δci is obtained that isthe difference between normalized closeness degree andaverage degree )en

Δci ci minus1n

(21)

When Δci gt 0 it means that the memberrsquos contribu-tion to the total revenue of the supply chain is higher thanthe average level and should be compensated accordinglyAt Δci 0 it means that the memberrsquos contributionis equal to the average level without compensation ordeduction Δci lt 0 means that the memberrsquos contributionto the total revenue of the supply chain is below theaverage level and the corresponding revenue should bededucted

)e above method can be combined with the incomedistribution coefficient to optimize then the followingexists

φi(v) φi (v) + Δci times v(I) + b (22)

It is necessary to prove whether the modified Shapleyvalue φi(v) meets the necessary conditions for successfulcooperation )e proof formula is as follows

1113944φiprime(v) 1113944φi(v) + 1113944Δciv(I)

1113944φi(v) + v(I) 1113944 ci minus1n

1113944φi(v) v(I)

(23)

Obviously the modified Shapley value can meet thenecessary conditions for successful cooperation )ereforethis improved method is feasible Moreover it is morereasonable to judge the contribution to the total revenue ofthe supply chain by considering factors such as risk takinginnovation level and resource input )erefore the benefitdistribution results of the e-commerce supply chainmeet therequirements of all parties under the concept of sustainabledevelopment

So far the benefit distribution algorithm design ofe-commerce supply chain under the concept of sustainabledevelopment has been completed

3 Experimental Design and Result Analysis

In order to verify the effectiveness of the profit distributionalgorithm of e-commerce supply chain under the concept ofsustainable development designed in this paper a simulationexperiment is needed to design the specific experimentalscheme as follows

In order to make the experimental results reflect the realsituation the experiment needs to be carried out in the sameenvironment )e specific experimental environment isshown in Table 3

Table 2 Average random consistency index assignment criteria

n 1 2 3 4 5 6 7 8 9 10RI 0 0 058 090 112 124 132 141 145 149

Table 1 Hierarchical evaluation criteria for the importance ofindicators

Ratio factor Quantitative valuesAs important 1A little important 3More important 5Highly important 7Extremely important 9)e intermediate value of two adjacentjudgments 2 4 6 8


)e experimental data came from a large e-commercesupply chain platform )e background data were taken asthe experimental sample data and the collected data werecleaned and repaired to improve the accuracy of the sim-ulation experiment

)e income distribution coefficient of the relative errorrate calculated by the validation electricity supply chainprofit distribution algorithm is one of the important indi-cators )erefore first compare the electricity supply chainbased on cloud gravity method interest allocation algorithmbased on the technique of block chain electricity supplychain profit allocation algorithm and the concept of sus-tainable development under the electricity supply chainprofit distribution algorithm for calculating the relative errorrate and income distribution coefficient )e comparisonresults are shown in Figure 1

As shown in Figure 1 with the increase of the number ofexperiments the relative error rates of calculation of incomedistribution coefficient of the three algorithms show differenttrends Among them the relative error rates of calculation ofincome distribution coefficient of the distribution algorithmbased on cloud barycenter method vary between 181 and225 )e relative error rate of income distribution coeffi-cient calculation of the allocation algorithm based on blockchain technology varies between 172 and 216 while thatof the allocation algorithm based on sustainable development

concept varies between 71 and 49 which is the lowestamong the three methods )is shows that the algorithm canobtain accurate calculation results of the profit distributioncoefficient of the e-commerce supply chain laying a solidfoundation for the subsequent profit distribution of thee-commerce supply chain

Electricity supply chain profit distribution precision tovalidate electricity supply chain profit allocation algorithmperformance is another important indicator )erefore wecompared the electricity supply chain based on cloud gravitymethod interest allocation algorithm based on the tech-nique of block chain electricity supply chain profit allocationalgorithm and the concept of sustainable development underthe electricity supply chain profit distribution precision ofthe algorithm and the results are as shown in Table 4

According to the analysis of the data in Table 4 theelectricity supply chain based on cloud gravity method in-terests allocation algorithm of electricity supply chain dis-tribution of average accuracy is 835 and the electricitysupply chain based on block chain technology interestsallocation algorithm of electricity supply chain distributionof average accuracy is 766 which is the lowest in the threealgorithms and compared with the two types of algorithmsthe average accuracy of profit distribution of the e-com-merce supply chain based on the allocation algorithm underthe concept of sustainable development is 948 It is the

Table 3 Experimental environment

Configuration ParameterCPU Intel (R) Core (TM) i5-9400Frequency 290GHzRAM 160GBOperating system Windows 10Digits 64 bitSimulation software MATLAB 70

25

20

15

10

5

0

Relat

ive e

rror

rate

()

10 20 30 40 50 60 70 80 90 100Number of experiments

Profit distribution algorithm of e-commerce supply chain based on cloud barycenter methodProfit distribution algorithm of e-commerce supply chain based on block chain technologyProfit distribution algorithm of e-commerce supply chain under the concept of sustainable development

Figure 1 Comparison result of relative error rate of calculation of income distribution coefficient


highest among the three algorithms indicating that thealgorithm can achieve accurate interest distribution ofe-commerce supply chain and reduce related interestdisputes

Finally the time cost of the e-commerce supply chainprofit distribution algorithm based on cloud barycentermethod the e-commerce supply chain profit distributionalgorithm based on block chain technology and thee-commerce supply chain profit distribution algorithmunder the concept of sustainable development are comparedand the comparison results are shown in Figure 2

By analyzing the data in Figure 2 it can be seen that thetime cost of the e-commerce supply chain profit distributionalgorithm based on cloud barycenter method changes withinthe range from 141 s to 302 s and that of the e-commercesupply chain profit distribution algorithm based on block

chain technology changes within the range from 075 s to 295Compared with these two algorithms under the concept ofsustainable development the time cost of the profit distri-bution algorithm of the e-commerce supply chain is alwaysbelow 05 s which is far lower than the profit distributionalgorithm of the e-commerce supply chain based on the cloudbarycenter method and the profit distribution algorithm ofthe e-commerce supply chain based on the block chaintechnology indicating that the time cost of the algorithm issmaller and the overall efficiency is higher

4 Conclusion

Electronic commerce is considered to be the new engine ofeconomic growth the network of the impact of a new type ofeconomic activity and opportunity is unprecedented

Table 4 Comparison of distribution accuracy (unit )

Number ofexperiments

Profit distribution algorithm of e-commerce supply chain based oncloud barycenter method ()

Profit distribution algorithm of e-commerce supply chain based on

block chain technology ()

Profit distribution algorithm of e-commerce supply chain under the

concept of sustainable development ()10 865 752 95620 842 784 94830 813 741 96340 863 732 94150 842 696 95260 816 853 93970 821 742 96280 847 841 94790 805 753 939100 836 764 928Average 835 766 948

35

30

25

20

15

10

05

0

Tim

e cos

t (s)

0 10 20 30 40 50 60 70 80 90 100Number of experiments


Figure 2 Time cost comparison


traditional economics is facing the correction and the oldcompetition rules are unravelling so a new kind of theory tosystematically study and guide the e-commerce is urgentlyneeded )us electricity supply chain arises at the historicmoment but the sharpening contradictions between therelevant members of the supply chain profit distribution arenot rational )erefore there is a need for electricity supplychain profit allocation algorithm in order to solve theseproblems but the traditional algorithm of electricity supplychain profit distribution has various problems such as lowaccuracy and time cost As a result it raised the concept ofsustainable development under the electricity supply chainprofit allocation algorithm Experimental results show thatthe relative error rate after calculation of the income dis-tribution coefficient of this algorithm varies between 71and 49 the average distribution accuracy is 948 and thetime cost is always below 05 s )e contributions of thispaper are low relative error rate after calculation of theincome distribution coefficient high collocation accuracyand short time cost which can fully solve the problemsexisting in the traditional algorithm It promotes the har-monious and prosperous development of e-commercesupply chains

5 Limitations

)e limitations of this paper includes that up till now it canprovide a low relative error rate up to 49 However thiscan be further decreased with more research and practicalapproach )e average distribution accuracy ideally shouldbe 100 but in real time scenarios it may drop a little andobtain a value of 948 So this accuracy can be furtherimproved

6 Future Scope

)is profit distribution algorithm based on e-commercesupply chain can be extended up to higher levels as it canfurther optimize the profits in electronic commerce )iswould really help out the economic activities going on

Data Availability

)e data used to support the findings of this study areavailable from the corresponding author upon request

Conflicts of Interest

)e author declares that he has no conflicts of interest

Acknowledgments

)is work was supported by the Jiangsu Overseas VisitingScholar Program for University Prominent Young andMiddle-Aged Teachers and Presidents Research Project ofPhilosophy and Social Sciences in Jiangsu Universities andResearch on Jiangsu Logistics Safety Production Manage-ment from the Perspective of Sustainable Supply ChainManagement (no 2019sja1859)

References

[1] A S Al-Adwan M Alrousan and A Al-Soud ldquoRevealing theblack box of shifting from electronic commerce to mobilecommerce the case of Jordanrdquo Journal of eoretical andApplied Electronic Commerce Research vol 14 no 1 pp 51ndash67 2019

[2] A Jannah and H Hassanah ldquoE-commerce in supply chainrdquoIOP Conference Series Materials Science and Engineeringvol 879 no 1 pp 12132ndash12143 2020

[3] L Wang M Gao and Z Liang ldquoApplication of data en-velopment and Internet of things technology for asset valueevaluationrdquoMobile Information Systems vol 2021 Article ID9934090 8 pages 2021

[4] J X Du and N C Tong ldquoResearch on income distribution ofaccounts receivable financing based on B2B e-commerceplatformrdquo Logistics Technology vol 42 no 12 pp 156ndash1592019

[5] X M Huang and S Yang ldquoResearch on collaborative benefitdistribution strategy of green supply chain-shapley valuecorrection model based on blockchain technologyrdquo Journal ofTechnology Economics and Management vol 14 no 8pp 14ndash19 2020

[6] M Yu T Quan Q Peng X Yu and L Liu ldquoA model-basedcollaborate filtering algorithm based on stacked AutoEn-coderrdquo Neural Computing amp Applications vol 5 2021

[7] H Peng ldquoResearch on credit evaluation of financial enter-prises based on the genetic backpropagation neural networkrdquoScientific Programming vol 2021 Article ID 7745920 8 pages2021

[8] J-H Zhao D-L Zeng T-W Zhou Y Hui and N SunldquoAnalysis of factors affecting the profits of closed-loop supplychain members under different subsidy objectsrdquo ComputerSystems Science and Engineering vol 35 no 3 pp 127ndash1392020

[9] T V S R K Prasad T Veeraiah Y Kiran K Srinivas andC Srinivas ldquoDecentralized production-distribution planningin a supply chain computer experimentsrdquo Materials TodayProceedings vol 18 no 1 pp A1ndashA11 2019

[10] S Rao R Nilakantan D Iyengar and K B Lee ldquoOn theviability of fixing leaky supply chains for the poor throughbenefit transfers a call for joint distributionrdquo Journal ofBusiness Logistics vol 40 no 2 pp 145ndash160 2019

[11] X Yu Y Chu F Jiang Y Guo and D Gong ldquoSVMs clas-sification based two-side cross domain collaborative filteringby inferring intrinsic user and item featuresrdquo Knowledge-Based Systems vol 141 pp 80ndash91 2018

[12] R Isaaks B Colby and A Dinar ldquoEmpirical application ofrubinstein bargaining model in western US Water transac-tionsrdquo Water Economics and Policy vol 12 no 1 pp 1ndash252019

[13] J Seol and H I Son ldquoBargaining model-based coverage areasubdivision of multiple UAVs in remote sensingrdquo Journal ofBiosystems Engineering vol 45 no 4 pp 133ndash144 2020

[14] Q Qu C Liu and X Bao ldquoE-commerce enterprise supplychain financing risk assessment based on linked data miningand edge computingrdquo Mobile Information Systems vol 2021Article ID 9938325 19 pages 2021

[15] X Yu J Yang and Z Xie ldquoTraining SVMs on a bound vectorsset based on Fisher projectionrdquo Frontiers of Computer Sciencevol 8 no 5 pp 793ndash806 2014

[16] T Ouyang and X Lu ldquoClustering analysis of risk divergenceof China governmentrsquos debtsrdquo Scientific Programmingvol 2021 Article ID 7033597 9 pages 2021


[17] P Sun ldquoInventory cost control model of fresh products basedon Reinforcement Learningrdquo Computer Simulation vol 37no 8 p 198 2020

[18] D Sharapov P Kattuman D Rodriguez and F J VelazquezldquoUsing the SHAPLEY value approach to variance decom-position in strategy research diversification internationali-zation and corporate group effects on affiliate profitabilityrdquoStrategic Management Journal vol 42 no 3 pp 608ndash6232021


design a kind of electricity supply chain based on cloudgravity method benefit allocation algorithm )e algorithmmainly studies the influence members of the electricitysupply chain financing income distribution the main factorsand the level of risk the degree of information sharing theeffort level and the audit supervision costs into the cloudgravity method On this basis the revenue distributionmodel is built to complete the design of the profit distri-bution algorithm of the e-commerce supply chain Howeverthe time cost of the profit distribution algorithm of thee-commerce supply chain is large and the overall efficiency islow Reference [5] designs a benefit distribution algorithm ofe-commerce supply chain based on block chain technologySix factors including resource contribution status effectrisk taking additional subsidy implementation degree andinnovation effort are proposed to influence benefit distri-bution and then a modified Shapley value model is estab-lished At the same time blockchain technology is applied tosolve problems such as mistrust among supply chainmembers and difficulties inmonitoring transaction data)einformation transparency and immutability provided byblockchain make the revised profit distribution strategymore fair credible and enforceable However the calcu-lation of the coefficient of income distribution of the al-gorithm has relatively low error rate so the practicalapplication effect is not good

At this stage the relevant leaders have stressed in manyoccasions on the concept of sustainable development whichrequires the electricity in lower costs At the same time itmust pay attention to the scientific methods and cannot relyon the expense of the damage to the ecosystem in order torealize the coordination of enterprise society environmentand sustainable development )is is the concept of sus-tainable development as the basic requirement of the electricbusiness enterprise )erefore this paper designs ane-commerce supply chain benefit distribution algorithmunder the concept of sustainable development and verifiesthe effectiveness of the algorithm through experiments

2 Design of Profit Distribution Algorithm forE-Commerce Supply Chain

)e word ldquobenefitrdquo has many different meanings and eventhe interpretation of the benefit varies widely in differentenvironments Other relevant experts and scholars havedefined this that is interest belongs to the category of socialrelations in essence and it is a kind of need in essence whichcan satisfy the psychological and behavioral demands of allclasses and groups All groups obtain psychological andbehavioral satisfaction to maintain their own survival anddevelopment which can meet the needs of different groupsof objects Profit distribution refers to the fact that in variouscooperative relationships the participants share their dueshare from the total income or profits generated by coop-eration [6] )e benefit distribution of the supply chainmeans that each member of the supply chain takes its dueshare from the total profit or revenue created by the supplychain alliance Of course each member of the supply chainshould reasonably share the total cost of the supply chain [7]

)e profit distribution of supply chain in this paper mainlyrefers to the profit increased by participating in cooperationCooperation is the prerequisite of benefit distribution and areasonable benefit distribution scheme is the guarantee ofstable cooperative relationship For supply chain membersjoining the supply chain alliance can not only enjoy variousshared resources brought by collaboration but also improveefficiency and reduce costs so as to gain more benefits [8]However combined with the current situation of e-com-merce supply chain cooperation its profit distributionmodel is often difficult to put into practice technically)erefore how to make a reasonable benefit distributionscheme of supply chain has become an imminent problem ofsupply chain management

21 Calculation of Income Distribution Coefficient )esupply chain alliance and its members distribute the cost andbenefit and the final amount allocated by both parties mustnot be lower than the basic expectation of each party beforeit can be accepted by both parties From the rational manhypothesis in the bargaining theory maximizing their ownbenefits is the code of conduct for both parties In order togain a beneficial amount of benefits both parties generallywill not easily choose to compromise [9] In the process ofbargaining both parties take turns to bid One party makesthe first bid and the other party can reject or accept it Onlywhen one partyrsquos bid is accepted by the other party thebargaining process ends that is both parties reach thedistribution amount satisfying their own interests [10]Suppose that after this process the equilibrium biddingstrategy of supply chain alliance and its members is obtainedwhich is represented by Pc1 and Pe1prime respectively

)ere will be three situations when the size of Pc1 + Pe1primeis compared with the size of revenue cakeNPV(P) minus I(1 + R) where NPV(P) represents the netpresent value of the income distribution I represents thetotal investment and R represents the minimum expectedreturn on investment

Case 1 Pc1 + Pe1prime is less than cake NPV(P) minus I(1 + R)Both the supply chain alliance and its members have a

certain understanding of the relevant cost data or benefits soany party in its first bid its bidding strategy set will refer to thedata or benefits they have obtained At this time the sum of thebidding strategy values of the supply chain alliance and itsmembers in the case of their first bid Pc1 + Pe1prime is probably lessthan the total cake [11] In this way I(1 + R) + Pc1 can bedefined as the lower limit of the income distribution intervaland the upper limit of the new income distribution interval anda new income distribution interval (I(1 + R) + Pc1NPV(P) minus

Pe1prime ) can be obtained which is Pc1 + Pe1prime smaller than theoriginal income distribution interval

Under the new income distribution interval repeat theabove analysis process that is distribute the ldquoreduced in-come cakerdquo again and a new income distribution intervalwill be obtained In Rubensteinrsquos bargaining model [12] thepatience factor is introduced that is with each incomedistribution the reduction of ldquocakerdquo and the extension of





(1)




(2)



(3)





Plowastc 1113944

h

i1Pci + αC (5)


I(1 + R) were

Plowaste 1113944

h

i1Pei + αC (6)









lowastc1113858 1113859

NPV(P) (7)






v(ϕ) 0







n

1113944

n

i1Xi v(I)

Xi ge v(i)

(9)




W |s| v(s) minus vs

i1113874 11138751113874 11138751113876 1113877 (10)








max aj minus min aj

(11)


B



⋮ ⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(12)



R



⋮ ⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(13)


Wijprime

aij

1113936nj1 aij

(14)


Wi

1113937

nj1 Wijprimen

1113969

1113936ni1

1113937

nj1 Wijprimen

1113969

λmax 1113944n

i1

(RW)i

nWi

(15)




CI λmax minus n

n minus 1 (16)


CR CIRI

(17)



1 a+2 a+


(aminus1 aminus



ai+

1113944

m

j1λj bij minus bj

+1113872 1113873

2

11139741113972

aiminus

1113944

m

j1λj bij minus bj

minus1113872 1113873

2

11139741113972

(18)



βi a

minusi

a+i + a

minusi

(19)


ci βi

1113936ni1 βi

(20)


Δci ci minus1n

(21)






1113944φi(v) + v(I) 1113944 ci minus1n

1113944φi(v) v(I)

(23)







n 1 2 3 4 5 6 7 8 9 10RI 0 0 058 090 112 124 132 141 145 149












25

20

15

10

5

0

Relat

ive e

rror

rate

()









4 Conclusion









35

30

25

20

15

10

05

0

Tim

e cos

t (s)






5 Limitations


6 Future Scope


Data Availability




Acknowledgments


References
























(1)




(2)



(3)





Plowastc 1113944

h

i1Pci + αC (5)


I(1 + R) were

Plowaste 1113944

h

i1Pei + αC (6)









lowastc1113858 1113859

NPV(P) (7)






v(ϕ) 0







n

1113944

n

i1Xi v(I)

Xi ge v(i)

(9)




W |s| v(s) minus vs

i1113874 11138751113874 11138751113876 1113877 (10)








max aj minus min aj

(11)


B



⋮ ⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(12)



R



⋮ ⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(13)


Wijprime

aij

1113936nj1 aij

(14)


Wi

1113937

nj1 Wijprimen

1113969

1113936ni1

1113937

nj1 Wijprimen

1113969

λmax 1113944n

i1

(RW)i

nWi

(15)




CI λmax minus n

n minus 1 (16)


CR CIRI

(17)



1 a+2 a+


(aminus1 aminus



ai+

1113944

m

j1λj bij minus bj

+1113872 1113873

2

11139741113972

aiminus

1113944

m

j1λj bij minus bj

minus1113872 1113873

2

11139741113972

(18)



βi a

minusi

a+i + a

minusi

(19)


ci βi

1113936ni1 βi

(20)


Δci ci minus1n

(21)






1113944φi(v) + v(I) 1113944 ci minus1n

1113944φi(v) v(I)

(23)







n 1 2 3 4 5 6 7 8 9 10RI 0 0 058 090 112 124 132 141 145 149












25

20

15

10

5

0

Relat

ive e

rror

rate

()









4 Conclusion









35

30

25

20

15

10

05

0

Tim

e cos

t (s)






5 Limitations


6 Future Scope


Data Availability




Acknowledgments


References






















v(ϕ) 0







n

1113944

n

i1Xi v(I)

Xi ge v(i)

(9)




W |s| v(s) minus vs

i1113874 11138751113874 11138751113876 1113877 (10)








max aj minus min aj

(11)


B



⋮ ⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(12)



R



⋮ ⋮ ⋱ ⋮


⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦

(13)


Wijprime

aij

1113936nj1 aij

(14)


Wi

1113937

nj1 Wijprimen

1113969

1113936ni1

1113937

nj1 Wijprimen

1113969

λmax 1113944n

i1

(RW)i

nWi

(15)




CI λmax minus n

n minus 1 (16)


CR CIRI

(17)



1 a+2 a+


(aminus1 aminus



ai+

1113944

m

j1λj bij minus bj

+1113872 1113873

2

11139741113972

aiminus

1113944

m

j1λj bij minus bj

minus1113872 1113873

2

11139741113972

(18)



βi a

minusi

a+i + a

minusi

(19)


ci βi

1113936ni1 βi

(20)


Δci ci minus1n

(21)






1113944φi(v) + v(I) 1113944 ci minus1n

1113944φi(v) v(I)

(23)







n 1 2 3 4 5 6 7 8 9 10RI 0 0 058 090 112 124 132 141 145 149












25

20

15

10

5

0

Relat

ive e

rror

rate

()









4 Conclusion









35

30

25

20

15

10

05

0

Tim

e cos

t (s)






5 Limitations


6 Future Scope


Data Availability




Acknowledgments


References






















CI λmax minus n

n minus 1 (16)


CR CIRI

(17)



1 a+2 a+


(aminus1 aminus



ai+

1113944

m

j1λj bij minus bj

+1113872 1113873

2

11139741113972

aiminus

1113944

m

j1λj bij minus bj

minus1113872 1113873

2

11139741113972

(18)



βi a

minusi

a+i + a

minusi

(19)


ci βi

1113936ni1 βi

(20)


Δci ci minus1n

(21)






1113944φi(v) + v(I) 1113944 ci minus1n

1113944φi(v) v(I)

(23)







n 1 2 3 4 5 6 7 8 9 10RI 0 0 058 090 112 124 132 141 145 149












25

20

15

10

5

0

Relat

ive e

rror

rate

()









4 Conclusion









35

30

25

20

15

10

05

0

Tim

e cos

t (s)






5 Limitations


6 Future Scope


Data Availability




Acknowledgments


References





























25

20

15

10

5

0

Relat

ive e

rror

rate

()









4 Conclusion









35

30

25

20

15

10

05

0

Tim

e cos

t (s)






5 Limitations


6 Future Scope


Data Availability




Acknowledgments


References

























4 Conclusion









35

30

25

20

15

10

05

0

Tim

e cos

t (s)






5 Limitations


6 Future Scope


Data Availability




Acknowledgments


References






















5 Limitations


6 Future Scope


Data Availability




Acknowledgments


References





















a novel algorithm for profit distribution of sustainable

Documents